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AI

AI agents that hold up in production, not just in the demo video.

Autonomous agents that are reliable, evaluated, and cost-controlled.

Request a quote See how we deliver
The problem

An agent that works in a demo and an agent you can put in front of customers are separated by everything that is hard about AI engineering: reliability on inputs you did not anticipate, cost that does not spiral when usage grows, evaluation you can actually trust, and guardrails that hold when a user, or an attacker, pushes on them.

Most agent projects stall here. The prototype is exciting; the path to something dependable is unglamorous engineering that the original demo never required.

Our approach

We build agents as software systems with AI inside, not prompts with hope around them. Architectures use explicit planning and tool use, structured outputs, and bounded autonomy. Every agent ships with an evaluation suite that gates deploys, observability into prompts and traces, and defences against prompt injection and abuse.

Cost is treated as a first-class constraint, with model routing, caching and token budgets, so a successful launch does not become a runaway bill. Where agents act on-chain, every action runs through hard, auditable controls.

Scope of engagement

We build production AI agents and the infrastructure around them: autonomous and multi-agent systems with tool use and planning, retrieval over proprietary data, evaluation and guardrail pipelines, and cost-optimised inference. For crypto-native clients we connect agents to on-chain execution behind strict policy, signing, and spending controls.

Technology

The stack we build on

Proven tools, chosen for security, performance and long-term maintainability rather than novelty.

Python TypeScript OpenAI LangGraph pgvector OpenTelemetry Temporal Redis
Methodology

How we deliver

A disciplined, transparent sequence from first conversation to a monitored production system.

  1. 01

    Task & eval definition

    We define what success means and how it will be measured before building.

  2. 01

    Agent architecture

    Planning, tool use, and retrieval designed for bounded, reliable autonomy.

  3. 01

    Guardrails & evaluation

    Injection defences, abuse controls, and an eval suite that gates every deploy.

  4. 01

    Cost & latency optimisation

    Model routing, caching, and token budgets tuned against real traffic.

  5. 01

    Production rollout

    Deployed with tracing, cost dashboards, and a human-in-the-loop fallback.

Proof

Where we have shipped this

Selected engagements that put this capability into production.

All case studies
Trading Firms
Cobalt Trading

An AI Execution Agent That Cut Slippage 31% Across Venues

A quantitative trading firm wanted to automate large-order execution across fragmented crypto venues. We built an AI execution agent that ad...

-31% Realised slippage reduction
FAQ

Common questions

Still unsure? A senior engineer will answer the specifics on a short scoping call.

Ask us directly

Bounded autonomy, structured tool interfaces, input and output guardrails, and — for any irreversible action like an on-chain transaction — hard policy checks, spending limits, and human-in-the-loop approval where the stakes justify it. Autonomy is a dial we set deliberately, not a default.

We route to the cheapest model that meets the quality bar for each step, cache aggressively, set per-request token budgets, and expose cost dashboards so spend is observable. We routinely cut inference costs substantially versus a naïve single-model implementation.
Request a quote

Scope your ai agent development engagement

Tell us what you are building. We will respond with a senior engineer's assessment, a realistic timeline, and a fixed-scope proposal — typically within two business days.

  • A direct line to the engineers who will deliver
  • No obligation, no sales pressure, no junior hand-off
  • Strict confidentiality — NDA available on request

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